Detection of Immobilized Contrast Agent in Medical Imaging Applications Based on Flow Dynamics Analysis

ABSTRACT

A system for facilitating the detection of an immobilized contrast agent in medical imaging applications is proposed. The system includes means for providing a sequence of a total number of input images obtained at corresponding acquisition instants by imaging a body-part of a patient subjected to an administration of a contrast agent capable of circulating within the patient and of being substantially immobilized on a biological target, each input image including a plurality of input values each one indicative of a response to an interrogation signal of a corresponding portion of the body-part possibly including said contrast agent, and means for reducing a contribution of the circulating contrast agent within the body-part in at least one selected input image; the means for reducing includes means for creating a filtered image corresponding to each selected input image by replacing a set of input values of the selected input image with a set of corresponding filtered values, each filtered value being representative of the lowest response of the corresponding portion of the body-part in a set of multiple input images including the selected input image, the set of multiple input images consisting of a number of input images lower than the total number.

FIELD OF THE INVENTION

The present invention relates to the medical imaging field. Morespecifically, the present invention relates to the detection of animmobilized contrast agent.

BACKGROUND OF THE INVENTION

Medical imaging is a well-established technique (in the field ofequipments for medical applications), which allows analyzing a body-partof a patient in a substantially non-invasive manner. A specific medicalimaging technique is based on the recording of an echo signal thatresults from the application of ultrasound waves to the body-part. Thistechnique can advantageously be implemented with the administration ofan ultrasound contrast agent (UCA) to the patient (for example,consisting of a suspension of phospholipid-stabilized gas-filledmicrobubbles); as the contrast agent acts as an efficient ultrasoundreflector, it enhances the visualization of a vascular system within thebody-part where it is present.

Contrast agents, adapted to reach a specific (biological) target andthen remain immobilized thereon, have also been proposed in the lastyears for facilitating the detection of specific pathologies.Particularly, these contrast agents are capable of attaching to selectedtissues or receptors by means of a specific interaction therewith; forexample, the desired behavior can be achieved by incorporating atargeting ligand in the formulation of the contrast agent (e.g., capableof interacting with tumoral tissues). In addition, contrast agents mayalso be conveyed or accumulated to a specific location, such as tissuesor organs, by means of a non-specific interaction therewith; forexample, the contrast agent may be recognized as a foreign substance bythe immune system of the patient and then moved to the liver for itsmetabolism and elimination. In any case, once the contrast agent (eitherwith specific or non-specific interaction) has reached the targetremaining immobilized thereon, its detection may allow distinguishingpathologies that would be otherwise difficult to identify.

A possible problem associated with the detection of the immobilizedcontrast agent is that only a relatively small fraction of the totalamount of the administered contrast agent actually reaches the target;conversely, most of the contrast agent continues to circulate (forexample, until it is filtered out by the lungs and/or in the liver ofthe patient). The echo signal that is measured is then the result ofdifferent contributions, which are due to the immobilized contrastagent, to the circulating (free-flowing) contrast agent and to thesurrounding tissues. Therefore, it is quite difficult to distinguish theecho signal generated by the immobilized contrast agent from the onegenerated by the circulating contrast agent and the tissues;particularly, it is almost impossible to differentiate the lowconcentration of immobilized contrast agent (often consisting of singleparticles thereof that reach the target individually) from the higherconcentration of circulating contrast agent. This adversely affects thespatial delineation and the quantification of the immobilized contrastagent, thereby hindering the correct detection of the pathologies ofinterest.

Attempts have been made to improve the discrimination of the immobilizedcontrast agent. For example, “P. A. Dayton, D. Pearson, J. Clark, S.Simon, P. Schumann, R. Zutshi, T. Matsunaga, K. W. Ferrara, UltrasonicEnhancement of α_(v)β₃ Expressing-Cells With Targeted Contrast Agents,2003 IEEE Ultrasonics Symposium” proposes a solution that is based onthe observation that the echo signal corresponding to the immobilizedcontrast agent has a bandwidth that is narrower than the one of thecirculating contrast agent This document then mentions the possibilityof discriminating the different contributions in the echo signalexploiting the larger bandwidth that is observed for the circulatingcontrast agent.

However, no solution is available in the art for detecting theimmobilized contrast agent with an acceptable degree of accuracy.Particularly, the problem of efficiently discriminating the immobilizedcontrast agent from the circulating contrast agent is still unresolved.In this context, it would also be desirable to quantify theconcentration of the immobilized contrast agent at each location.

Therefore, nowadays it is necessary to wait until the circulatingcontrast agent has completely disappeared before being able to identifythe immobilized contrast agent; however, this requires a relatively longtime (up to tens of minutes).

All of the above hinders the clinical application of currently availablecontrast-specific imaging techniques for the detection of immobilizedcontrast agents.

SUMMARY OF THE INVENTION

The present invention proposes a solution that exploits the differencein flow dynamics between the immobilized contrast agent and thecirculating contrast agent.

Particularly, an aspect of the invention proposes a system forfacilitating the detection of an immobilized contrast agent in medicalimaging applications. The system includes means (such as an ultrasoundscanner) for providing a sequence of a total number of input images. Theinput images are obtained (at corresponding acquisition instants) byimaging a body-part of a patient subjected to an administration of acontrast agent (i.e., at least a portion of said images contains asignal generated by the contrast agent); the contrast agent is capableof being substantially immobilized (i.e., remaining in a substantiallyfixed position) on a biological target, being otherwise freelycirculating within the patient's body (at least up to the time of itselimination from or metabolization by the body); for example, thecontrast agent may attach selectively to specific tissues or receptors(by means of a specific interaction therewith) or it may be conveyed toor accumulated into specific tissues or organs (by means of anon-specific interaction therewith). Each input image includes aplurality of input values (i.e., pixel or voxel values); each inputvalue is indicative of a response to an interrogation signal (such as anecho signal for ultrasound waves) of a corresponding portion of thebody-part, which possibly includes said contrast agent. The systemfurther includes means for reducing a contribution of the circulatingcontrast agent within the body-part in one or more selected inputimages. For this purpose, a filtered image corresponding to eachselected input image is created. The result is achieved by replacing aset of input values of the selected input image (for example, in adesired region of interest) with a set of corresponding filtered values.Each filtered value is representative of the lowest response of thecorresponding portion of the body-part in a set of multiple input images(including the selected input image); the set of multiple input imagesconsists of a number of input images (two or more), which is lower thanthe total number.

In a preferred embodiment of the invention, each filtered value consistsof the minimum among the corresponding input values (in said set ofmultiple input images); for example, this result may be achieved byapplying a modified version of the Minimum Intensity Projection (Min_IP)algorithm.

Typically, the set of multiple input images consists of the selectedinput image and at least one preceding input image in the sequence.

Preferably, two or more preceding input images are taken into accountfor this purpose.

According to a preferred embodiment, the number of preceding inputimages is selected as a function of an estimated flow rate of thecontrast agent (for example, increasing or decreasing it when the flowrate is low or high, respectively).

In a particular implementation, it is also possible to temporallysub-sample the preceding input images.

Typically, a contribution of the tissues in the input images has beenalready substantially removed, or at least reduced (for example, byacquiring them with a contrast-specific imaging mode).

A way to further improve the solution is of subtracting a backgroundimage (for example, taken before the arrival of the contrast agent inthe body-part) from the input images.

In a preferred implementation, the set of multiple input images isspatially sub-sampled according to an estimated resolution thereof (forexample, based on the size of speckle grains that typically occur inultrasound imaging).

As a further improvement, it is possible to compensate a relative motionof each input image (with respect to a selected reference image).

Moreover, the input images may also be linearized (so as to make theirinput values substantially proportional to a concentration of thecontrast agent in the corresponding portions of the body-part).

The proposed solution is particularly advantageous when each filteredimage is displayed substantially in real-time with the acquisitioninstants of the corresponding input image (i.e., with a short delay butwithout waiting for the completion of the analysis process).

Preferably, the filtered images are overlaid onto the correspondingselected input images.

Another aspect of the present invention proposes a corresponding method.

A further aspect of the present invention proposes a computer programfor performing the method.

The characterizing features of the present invention are set forth inthe appended claims. The invention itself, however, as well as furtherfeatures and the advantages thereof will be best understood by referenceto the following detailed description, given purely by way of anon-restrictive indication, to be read in conjunction with theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a pictorial representation of an ultrasound scanner in whichthe solution according to an embodiment of the invention is applicable;

FIGS. 2 a-2 b are a schematic representation of an exemplary applicationof the solution according to an embodiment of the invention;

FIGS. 3 a-3 b are a schematic representation of a different exemplaryapplication of the solution according to the same embodiment of theinvention;

FIGS. 4 a-4 b are time diagrams explaining operation of the solutionaccording to an embodiment of the invention;

FIGS. 5 a-5 b are a schematic representation of the application of theminimum intensity projection algorithm known in the art;

FIGS. 6 a-6 b are a schematic representation of the application of themaximum intensity projection algorithm known in the art;

FIGS. 7 a-7 b are a schematic representation of an exemplary applicationof the solution according to an embodiment of the invention in acritical condition;

FIGS. 8 a-8 b are a schematic representation of an exemplary applicationof the solution according to an embodiment of the invention in anothercritical condition;

FIGS. 9 a-9 b are a schematic representation of an exemplary applicationof the solution according to a further embodiment of the invention;

FIGS. 10 a-10 b are a schematic representation of a different exemplaryapplication of the solution according to the same embodiment of theinvention;

FIG. 11 shows the simulation of an exemplary application of thedifferent algorithms;

FIGS. 12 a-12 d show an example of in-vivo application of the solutionaccording to an embodiment of the invention; and

FIG. 13 depicts the main software and hardware components that can beused for practicing the solution according to an embodiment of theinvention.

DETAILED DESCRIPTION

FIG. 1 represents an example of a medical imaging system according to anembodiment of the invention. In particular, said imaging system is anultrasound scanner 100, which includes a central unit 105 and ahand-held transmit-receive imaging probe 110 (for example, of the arraytype). The imaging probe 110 transmits ultrasound waves consisting of asequence of insonifying ultrasound pulses (for example, having a centerfrequency between 2 and 10 MHz), and receives a (raw) radio-frequency(RF) echo signal resulting from the reflection of the ultrasound pulses;for this purpose, the imaging probe 110 is provided with atransmit/receive multiplexer, which allows using the imaging probe 110in the pulse-echo mode.

The central unit 105 houses a motherboard 115, on which the electroniccircuits controlling operation of the ultrasound scanner 100 (such as amicroprocessor, a working memory and a hard-disk drive) are mounted.Moreover, one or more daughter boards (denoted as a whole with 120) areplugged on the motherboard 115; the daughter boards 120 provide theelectronic circuits for driving the imaging probe 110 and for processingthe received echo signal. The ultrasound scanner 100 can also beequipped with a drive 125 for reading removable disks 130 (such asfloppy-disks). A monitor 135 displays images relating to the analysis inprogress. Operation of the ultrasound scanner 100 is controlled by meansof a keyboard 140, which is connected to the central unit 105 in aconventional manner; preferably, the keyboard is provided with atrackball 145 that is used to manipulate the position of a pointer (notshown in the figure) on a screen of the monitor 135.

In ultrasound imaging applications, the imaging probe 110 is typicallyplaced in contact with the skin of a patient 150 in the area of abody-part 155 to be analyzed.

In general, a contrast agent capable of being immobilized on a selectedtarget is administered to the patient, so that the body-part underanalysis receives said contrast agent. Depending on the chosen imagingtechnique, the contrast agent can be specific for enhancing, forinstance, Ultrasound images, Magnetic Resonance images or X-ray ComputedTomography images. The contrast agent can be administered orally (forexample, for imaging the gastro-intestinal tract), via nebulizer intothe airways (for imaging the lungs), or by injection. Administration byinjection includes, for instance, intravascular (such as intravenous orintra-arterial), intralymphatic, subcutaneous, intramuscular,intradermal, intraperitoneal, interstitial, intrathecal or intratumoraladministration. Preferably the contrast agent is administered byinjection, intravenously, either as a continuous infusion (typically bymeans of a pump) or as a bolus (typically by hand with a syringe), sothat the body-part under analysis is perfused with said contrast agent.According to the present invention, and depending on the differentimaging techniques, the contrast agent can be administered to thepatient before and/or during the acquisition of the images of thebody-part.

The contrast agent is substantially free to circulate within thepatient, so as to be received by the body-part under analysis; forexample, the contrast agent can move along the gastrointestinal tract(in case of oral administration), or within the vascular system (in caseof intravascular administration). However, the contrast agent is alsocapable of being immobilized on a selected (biological) target, so as toremain in a substantially fixed position for the whole duration of theanalysis process (or at least a large portion thereof).

Preferably, the contrast agent is capable of enhancing ultrasoundimages, such as the images acquired with the system of FIG. 1.

Suitable contrast agents for ultrasound imaging include suspensions ofgas bubbles in a liquid carrier; typically, the gas bubbles havediameters on the order of 0.1-5 μm, so as to allow them to pass throughthe capillaries of the body of the patient. The gas bubbles aregenerally stabilized by entraining or encapsulating the gas or aprecursor thereof into a variety of systems, including emulsifiers,oils, thickeners, sugars, proteins or polymers; stabilized gas bubblesare referred to as gas-filled microvesicles. The microvesicles includegas bubbles dispersed in an aqueous medium and bound at the gas/liquidinterface by a very thin envelope involving a surfactant, i.e., anamphiphilic material (also known as microbubbles). Alternatively, themicrovesicles include suspensions in which the gas bubbles aresurrounded by a solid material envelope formed of lipids or of naturalor synthetic polymers (also known as microballoons or microcapsules).Another kind of contrast agent includes suspensions of porousmicroparticles of polymers or other solids, which carry gas bubblesentrapped within the pores of the microparticles. Examples of suitableaqueous suspensions of microvesicles, in particular microbubbles andmicroballoons, and of the preparation thereof are described inEP-A-0458745, WO-A-91/15244, EP-A-0554213, WO-A-94/09829 andWO-A-95/16467 (the entire disclosures of which are herein incorporatedby reference). An example of a commercial ultrasound contrast agentcomprising gas-filled microvesicles is SonoVue® by Bracco InternationalBV.

Preferably, the contrast agent is formulated in such a way as toselectively bind to a desired target by means of a specific interactiontherewith; in this case, it is commonly referred to as a targetedcontrast agent. For example, this behavior can be achieved byincorporating a targeting ligand capable of selectively binding (such asthrough biochemical affinity and/or electrostatic interaction) to adesired tissue or receptor. Examples of targeting ligands (which may beinserted into a membrane of the microbubbles) are monoclonal antibodies,peptides, or polysaccharides. The term tissue includes within itsmeaning individual cells as well as aggregates of cells, such asmembranes or organs. The term refers to either normal (healthy) orabnormal (pathological) cells or aggregates of cells. Examples oftissues are myocardial tissues (including myocardial cells andcardiomyocites), membranous tissues (such as endothelium andepithelium), and connective tissues; examples of pathological tissuesare infarcted heart tissues, blood clots, atherosclerotic plaques andtumoral tissues. The receptors include any molecular structure locatedon the tissues (for example, within the cells or on their surfaces),which is capable to selectively bind to a specific substance. Exemplaryreceptors are glycoprotein GPIIbIIIa or fibrin (for example, located inblood clots or thrombi) or KDR (for example, located in tumoraltissues). Examples of suitable targeted contrast agents and of targetingligands are described in “G. M. Lanza and S. A. Wickline, TargetedUltrasonic Contrast Agents for Molecular Imaging and Therapy, Progressin Cardiovascular Diseases, 44(1), 2001, 13-31”, and in the co-pendingInternational Patent Application No. PCT/EP2005/054041 filed on 17 Aug.2005 (the entire disclosures of which are herein incorporated byreference).

Also contrast agents without specific targeting ligands may neverthelessbe immobilized at specific locations of the patients by means ofnon-specific interactions therewith. For example, depending on theirformulation and size, certain gas-filled microvesicles may be rapidlyrecognized as non-self components of the blood, thus being opsonized byblood proteins and then phagocytosed by monocytes or macrophages. Inthis case, generally, a large fraction of the gas-filled microvesiclesends up in the liver. Alternatively, gas-filled microvesicles (which areless rapidly recognized as non-self components of the blood) may extendtheir circulation time up to at least 20 minutes. For these gas-filledmicrovesicles a slow accumulation thereof can occur in certain organs(for example, the kidney); therefore, at late times after administrationthe gas-filled microvesicles can be slowly moving or completely stoppedin these organs. The quantification of the above-described phenomenon,also known in the art as Late Phase Opacification (LPO), may providevaluable information about the status of the organs at issue.

With reference again to FIG. 1, a series of ultrasound pulses with lowacoustic energy (such as with a mechanical index MI=0.01-0.1) is appliedto the body-part 155, so as to involve a negligible destruction of thecontrast agent (such as less than 20%, and preferably less than 10% ofits total amount). The echo signal that is recorded in response to theultrasound pulses over time provides a representation of the evolutionof the body-part 155 during the analysis process (either while thepatient 150 undergoing the administration of the contrast agent or lateron). The echo signal is then converted into a sequence of digital images(or frames) in standard Brightness mode (B-mode), which images representthe body-part 155 at corresponding successive acquisition instants (forexample, with a sampling rate of 10-30 images per second). Each image isdefined by a bitmap consisting of a matrix (for example, with 512 rowsand 512 columns) of values for respective visualizing elements, i.e.,basic picture elements (pixels) or basic volume elements (voxels), eachone corresponding to a location consisting of a basic portion of thebody-part 155. Typically, the pixel value consists of a gray-scale level(for example, coded on 8 bits) defining the brightness of the pixel; thepixel value increases from 0 (black) to 255 (white) as a function of theintensity of the corresponding echo signal.

The echo signal and then the corresponding images generally result fromthe superimposition of different contributions, which are generated bythe contrast agent that is still circulating, the contrast agent that isimmobilized on the target, and the surrounding tissues. Preferably, theultrasound scanner 100 operates in a contrast-specific imaging mode soas to substantially remove, or at least reduce, the dominant (linear)contribution of the tissues in the echo signal, with respect to the(non-linear) contribution of the (circulating and immobilized) contrastagent; examples of contrast-specific imaging modes include harmonicimaging (HI), pulse inversion (PI), power modulation (PM) and contrastpulse sequencing (CPS) techniques, as described, for example, in “Rafteret al., Imaging technologies and techniques, cardiology Clinics 22(2004), pp. 181-197” (the entire disclosure of which is herewithincorporated by reference).

In order to allow discriminating the immobilized contrast agent from thecirculating contrast agent, the solution described in the followingstarts from the observation that the contribution of the immobilizedcontrast agent shows a high level of correlation over time, in contrastto the contribution of the circulating contrast agent that instead showsa low level of correlation. The highly correlated echo signal isrepresented in the sequence of images by corresponding pixel values (forthe same location) that exhibit small or no variation (from one instantto the other); conversely, the echo signal with low correlation isrepresented by corresponding pixel values that exhibit a high variation.The images are then processed so as to substantially suppress (or atleast attenuate) the pixel values showing high variations (at the sametime preserving the pixel values showing low variations).

For this purpose, each pixel value is replaced by the minimum betweenthe pixel value itself and the corresponding pixel value in thepreceding image. More formally, the pixel value is obtained by applyingthe following proposed algorithm:

OP(x,y,k)=MIN[IP(x,y,k),IP(x,y,k−1)],

where OP(x,y,k) is the (output) processed value of the pixel identifiedby the spatial coordinates x,y (numbers of row and column, respectively)in the image taken at the instant k, IP(x,y,k) and IP(x,y,k−1) are the(input) original values of the pixel in the same image and in thepreceding one, and MIN[ ] is a function determining the minimum betweenits arguments.

An example of application of the above-mentioned algorithm isrepresented schematically in FIGS. 2 a-2 b. Particularly, FIG. 2 a showsa portion (consisting of 5 pixels P₁-P₅) of exemplary images taken atconsecutive instants (T₁-T₈). For the sake of simplicity, each pixelP₁-P₅ is represented as completely black in the absence of contrastagent and completely white when the contrast agent is detected.

As shown in the figure, at the beginning (instant T₁) all the pixelsP₁-P₅ are black to indicate that no contrast agent is present in thecorresponding region of the body-part (assuming that the contribution inthe echo signal due to the tissues has been completely suppressed). Afirst particle of contrast agent (such as a microbubble) enters the sameregion at the instant T₂, as shown by the pixel P₁ that becomes white.The first particle of contrast agent then crosses the region from theleft to the right (white pixels P₂-P₅ at the instants T₃-T₆), and leavesit at the instant T₇. In addition to the first particle, a secondparticle of contrast agent enters the region (white pixel P₁) at theinstant T₅. The second particle of contrast agent moves to the nextpixel P₂ at the instant T₆, and then remains immobilized at thisposition (instants T₆-T₈).

The application of the proposed algorithm to the example described abovegenerates a corresponding image that is shown in FIG. 2 b. Particularly,every pixel P₁-P₅ that changes between consecutive images becomes black(since it is replaced by the corresponding minimum calculated by thealgorithm); conversely, when a pixel P₁-P₅ is stationary (betweenconsecutive images) it remains at its value. As a result, thecontribution of the first (circulating) particle of contrast agentdisappears; the second particle of contrast agent is not shown when itis moving (instants T₅-T₆), but it is detected as soon as it isimmobilized (instants T₇-T₈).

Naturally, in a real application each pixel can be represented by anygray-scale level. Particularly, the pixel values are a function of theconcentration of contrast agent; the pixels are quite dark in thepresence of low concentrations of contrast agent (such as when fewparticles thereof are immobilized) while they are very bright in thepresence of high concentrations of contrast agent (such as when thecirculating contrast agent enters the body-part under analysis duringthe wash-in phase).

For example, as shown in FIG. 3 a, at the beginning (instant T₁) all thepixels P₁-P₅ are black (no contrast agent). A particle of contrast agententers the region at the instant T₂ (as shown by the pixel P₁ thatbecomes gray); the particle of contrast agent moves to the next pixel P₂at the instant T₃, and then remains immobilized in this position(instants T₄-T₈). In the representation of FIG. 3 a, further circulatingparticles of contrast agent cross the region from the left to the rightat the instants T₄-T₈ (white pixels P₁-P₅) and at the instants T₆-T₈(white pixels P₁-P₃). As can be seen, the contribution of thecirculating contrast agent is predominant, and substantially hides thepresence of the immobilized contrast agent.

The application of the proposed algorithm to this example generates acorresponding image that is shown in FIG. 3 b. In this case as well, thecontribution of the circulating contrast agent disappears leaving onlythe contribution of the immobilized contrast agent (gray pixel P₂ at theinstants T₄-T₈).

In other words, the above-described processing filters out the(high-intensity) peaks of the pixel values over time. In order toexplain this behavior, an exemplary sequence of corresponding pixelvalues for the same location is shown in FIG. 4 a. As can be seen, aparticle of contrast agent is immobilized in the pixel location at theinstant about 2s (as indicated by the slight increase of the pixelvalue). A large number of circulating particles of contrast agent thencrosses the same pixel at the instants about 12s, 22s, 30s 38s, 46s, and54s (as indicated by the far higher increases of the pixel value).

However, as shown in FIG. 4 b, the application of the proposed algorithmresults in a pattern of the pixel values that is substantially flat(after the detection of the immobilized particle of contrast agent).Indeed, the peaks caused by the passage of the circulating particles ofcontrast agent are very short, so that the algorithm will always comparethe corresponding high pixel value with the preceding low pixel valuedue to the immobilized particle of contrast agent; in this way, thosepeaks disappear leaving the contribution of the immobilized particle ofcontrast agent only.

The proposed algorithm is derived from the Minimum Intensity Projection(Min_IP) algorithm, and it will be referred to as “modified Min_IPalgorithm” hereinafter. In the (original) Min_IP algorithm, each pixelvalue is replaced with the minimum between the pixel value itself andthe running minimum resulting from the earlier iterations of theprocess, that is:

OP(x,y,k)=MIN[IP(x,y,k),OP(x,y,k−1)],

where OP(x,y,k−1) is the (output) processed value of the pixel in thepreceding image.

However, it should be noted that with the Min_IP algorithm it isimpossible to detect the immobilized contrast agent. For example, inFIG. 5 a the same scenario provided in FIG. 2 a is taken intoconsideration.

The application of the Min_IP algorithm to this example generates acorresponding image that is shown in FIG. 5 b. Particularly, every pixelP₁-P₅ that has become black always maintains the same value (since theMin_IP algorithm replaces it with its running minimum). Therefore, whena particle of contrast agent is immobilized, the corresponding pixel(P₂) is not updated and remains black.

Similar considerations apply to the Maximum Intensity Projection(Max_IP) algorithm. In the Max_IP algorithm, each pixel value isreplaced with the maximum between the pixel value itself and the runningmaximum resulting from the earlier iterations of the process, that is:

OP(x,y,k)=MAX[IP(x,y,k),OP(x,y,k−1)],

where MAX[ ] is a function determining the maximum between itsarguments. However, even with the Max_IP algorithm it is impossible todetect the immobilized contrast agent. For example, in FIG. 6 a the samescenario provided in FIG. 2 a is taken into consideration again.

The application of the Max_IP algorithm to this example generates acorresponding image that is shown in FIG. 6 b. Particularly, every pixelP₁-P₅ that has become white always maintains the same value (since theMax_IP algorithm replaces it with its running maximum). Therefore, oncethe circulating contrast agent reaches a generic pixel P₁-P₅, this pixelremains white even when the circulating contrast agent has left it. As aresult, the circulating contrast agent completely masks the immobilizedcontrast agent (at the pixel P₂).

The Min_IP and the Max_IP algorithms are used in the medical imagingfield, for example, to improve the visualization of the vascular system(e.g., by applying the Min_IP algorithm before the administration of thecontrast agent and subsequently applying the Max_IP algorithm after theadministration of the contrast agent); particularly, this technique isdescribed for the production of (three-dimensional) projection images inU.S. Pat. No. 6,436,049. Another application of the Max_IP algorithm isdisclosed in U.S. Pat. No. 6,676,606; in this case, the Max_IP algorithmis used to project the trajectories of the particles of contrast agent,so as to allow detecting tiny blood vessels (especially useful when theyare crossed by individual particles of contrast agent).

Referring back to the modified Min_IP algorithm, it has been observedthat (although quite effective in discriminating the immobilizedcontrast agent in many practical applications) this algorithm might showsome limitations in specific critical conditions (e.g., when a highconcentration of circulating particles of contrast agent is present orwhen the circulating particles of contrast agent move slowly).

For example, the application of the modified Min_IP algorithm to anexemplary condition with a high concentration of circulating particlesof contrast agent is represented schematically in FIGS. 7 a-7 b.

Particularly, as shown in FIG. 7 a, at the beginning (instant T₁) allthe pixels P₁-P₅ are black (no contrast agent). A first circulatingparticle of contrast agent crosses the region from the left to the right(white pixels P₁-P₅ at the instants T₂-T₆); the first circulatingparticle of contrast agent is directly followed by a second circulatingparticle of contrast agent that crosses the same region from the left tothe right immediately afterwards (white pixels P₁-P₅ at the instantsT₃-T₇).

The application of the modified Min_IP algorithm to the exampledescribed above generates a corresponding image that is shown in FIG. 7b. As in the preceding case, the contribution of the first circulatingparticle of contrast agent disappears. However, this is not true for thesecond circulating particle of contrast agent (since every pixel P₁-P₅remains white for two consecutive instants as the circulating particlesof contrast agent cross it in succession); therefore, the contributiondue to the second circulating particle of contrast agent remains visiblein the image even after the application of the modified Min_IPalgorithm, with the risk of hindering the detection of any actuallyimmobilized particle of contrast agent in the same region.

Likewise, the application of the modified Min_IP algorithm to anexemplary condition with slowly moving circulating particles of contrastagent is represented schematically in FIGS. 8 a-8 b.

Particularly, as shown in FIG. 8 a, at the beginning (instant T₁) allthe pixels P₁-P₅ are black (no contrast agent). A slowly movingcirculating particle of contrast agent enters the region at the instantT₂, as shown by the pixel P₁ that becomes white; the slowly movingcirculating particle of contrast agent is again in the same position atthe next instant T₃. The slowly moving circulating particle of contrastagent then moves to the pixel P₂ (instants T₄-T₅), to the pixel P₃(instants T₆-T₇), and to the pixel P₄ (instant T₈).

The application of the modified Min_IP algorithm to the exampledescribed above generates a corresponding image that is shown in FIG. 8b. As can be seen, whenever a pixel remains stationary (i.e., the pixelP₁ at the instants T₂-T₃, the pixel P₂ at the instants T₄-T₅, and thepixel P₃ at the instants T₆-T₇) it becomes white (at the instants T₃, T₅and T₇, respectively). Therefore, the slowly moving circulating particleof contrast agent is seen as immobilized at these instants (therebyintroducing an artifact in the resulting image).

In other words, the modified Min_IP algorithm (in the form providedabove) is unable to discriminate the immobilized particles of contrastagent from the circulating particles of contrast agent that remainaround the same pixel location for a period longer than the timeinterval between two consecutive images (because they either are closeto each other or move slowly). Indeed, the peaks caused by the passageof the circulating particles of contrast agent that are too broad cannotbe removed (since they have exactly the same behavior as the immobilizedparticles of contrast agent).

However, the above-mentioned problem can be solved by increasing thefiltering length of the modified Min_IP algorithm (defined by the numberof images that are taken into consideration for calculating theminimum). For example, with a filtering length of 3 images, the formuladefining the modified Min_IP algorithm becomes:

OP(x,y,k)=MIN[IP(x,y,k),IP(x,y,k−1),IP(x,y,k−2)],

where IP(x,y,k−2) is the (input) original value of the pixel in the nextpreceding image. In this way, any particle of contrast agent will beconsidered immobilized only when it remains in the same location forthree or more consecutive images; therefore, this allows filtering outpeaks of the pixel values that are broader.

In order to explain this behavior, in FIG. 9 a the same scenarioprovided in FIG. 7 a is taken into consideration.

The application of the modified Min_IP algorithm with a filtering lengthof 3 images to this example generates a corresponding image that isshown in FIG. 9 b. As can be seen, every pixel P₁-P₅ is black (since thealgorithm replaces it with its minimum over the last three images).Therefore, the contribution of all the circulating particles of contrastagent now disappears (even if they cross the same pixel location at twoconsecutive instants).

Similar considerations apply by taking into consideration the scenarioprovided in FIG. 8 a (repeated in FIG. 10 a).

In this case as well, as shown in FIG. 10 b, all the pixels P₁-P₅ becomeblack. As a consequence, the contribution of the slowly movingcirculating particle of contrast agent is removed (even if it remains atthe same pixel location for two consecutive instants).

More generally, the modified Min_IP algorithm can be defined by thefollowing formula:

OP(x,y,k)=MIN[IP(x,y,k), . . . , IP(x,y,k−n)] with n>=1,

where n is the number of images specifying the filtering length. Thefiltering length n corresponds to a time window (given by the product ofthe filtering length n by the inverse of the sampling rate of theimages), which defines the degree of temporal low-pass filtering appliedby the modified Min_IP algorithm. Indeed, the modified Min_IP algorithmis able to remove any peak of the pixel values over time having a widthsmaller than the extent of the filtering window (while broader peaksremain partially visible). This improves the robustness of the method,thereby increasing the differentiability between immobilized contrastagent and circulating contrast agent.

The value of the filtering length n (and then of the filtering window)is tuned according to the opposed requirements of high accuracy and fastresponse of the analysis process. Particularly, higher values of thefiltering length n allow removing circulating particles of contrastagent at a high concentration or circulating particles of contrast agentmoving very slowly. However, this delays the instant at which anyimmobilized particle of contrast agent is detected (since thecorresponding pixel becomes white only after the particle of contrastagent has remained at the same location for the extent of thecorresponding filtering window); moreover, a pixel may remain blackafter turning black (even for a single instant) at intervals shorterthan the length of the filtering window (for example, because of a noisein the images).

Preferably, the filtering length n is selected dynamically according tothe specific application. Particularly, the filtering length n is set tolow values (for example, n=1-5 corresponding to a filtering window of0.1-0.5s for a sampling rate of 10 images per second) when the contrastagent has a high flow rate (such as in arteries); conversely, thefiltering length n is set to high values (for example, n=3-12corresponding to a filtering window of 0.3-1.2s for the same samplingrate of the images) when the contrast agent has a low flow rate (such asin capillaries).

The simulation of an exemplary application of the different algorithmsdescribed above is illustrated in FIG. 11. Particularly, the figureshows the results that are obtained on a synthetic dataset simulatingdifferent time instants of the passage of a volume of contrast agent(consisting of, for example, gas-filled microvesicles) over a targetregion (located in the center of the images). The left-most columnillustrates the situation before the contrast agent has reached thetarget region (wash-in phase), the middle column illustrates thesituation when the contrast agent overlaps the target region, and theright-most column illustrates the situation when the contrast agent haspassed the target region (wash-out phase).

Row (A) represents the original sequence. As can be seen, theimmobilized contrast agent (in the target region) can be differentiatedfrom the circulating contrast agent (and then detected) only after allthe circulating particles of contrast agent have left the target region.In the case the contrast agent is administered through a bolusinjection, this can take several minutes; for contrast agentadministered as an infusion, which typically lasts for 10 minutes, thewash-out starts only after the infusion has been stopped.

Row (B) instead represents the result obtained with the application ofthe Min_IP algorithm. In this case, the images always remain black aswas explained before; therefore, it is impossible to detect theimmobilized contrast agent.

The result obtained with the modified Min_IP algorithm (using afiltering length n=1) is shown in row (C). In this case, thecontribution of the circulating contrast agent is partially suppressed;however, the suppression is not complete and the immobilized particlesof contrast agent are clearly visible and differentiable from thecirculating particles of contrast agent only during the wash-out phase.

Row (D) represents the result obtained with the same modified Min_IPalgorithm but setting a higher value of the filtering length (i.e.,n=9). As can be seen, the contribution of the circulating contrast agentis now almost completely suppressed; therefore, it is possible to detectthe immobilized particles of contrast agent as soon as they remainimmobilized in the target region, without the need to wait for thewash-out phase of the contrast agent.

At the end, the result obtained with the Max_IP algorithm is shown inrow (E). As discussed above, in this case the projection of thetrajectory of each particle of contrast agent is nicely emphasized;however, those projected trajectories completely mask the immobilizedparticles of contrast agent (thereby making their detection impossible).

FIGS. 12 a-12 d now show an example of an in-vivo application of theabove described modified Min_IP algorithm. For this purpose, a thrombuswas induced in the abdominal artery of a guinea pig. This body-part wasanalyzed by means of an imaging probe of the linear array type (15L8)connected to a Sequoia ultrasound scanner (Siemens). The ultrasoundscanner was operated in the CPS mode. The transmit frequency andmechanical index used were 10 MHz and 0.20, respectively. A bolusinjection of contrast agent (consisting of fibrin-specific microbubbles)was administered at an initial instant t=0 min. The artery containingthe thrombus was continuously scanned for a period of 10 min, includingthe wash-in and the wash-out of the contrast agent. The images soobtained were recorded on tape using a digital video recorder andprocessed off-line. In FIGS. 12 a-12 d, the original images are shown onthe left-hand side. The modified Min_IP algorithm (with a filteringlength n=9) was applied in a region of interest (ROI), which consistedof a rectangle including part of the artery with the thrombus; theresults obtained were superimposed on the original images, so as toobtain the (processed) images shown on the right-hand side of thefigures.

Particularly, FIG. 12 a relates to the instant immediately after theinjection of the contrast agent. As can be seen, the selected rectanglein the corresponding processed image is completely black, since thecontrast agent has not yet reached the corresponding region of thebody-part.

FIG. 12 b depicts the situation 2 minutes after the injection of thecontrast agent (during the wash-out phase). It is clear from theoriginal image that the immobilized particles of contrast agent attachedto the thrombus cannot be differentiated from the high concentration ofcirculating particles of contrast agent. In contrast, in the processedimage the contribution of the circulating particles of contrast agent isnicely suppressed; in this way, the immobilized particles of contrastagent are clearly visible, perfectly outlining the thrombus.

As shown in FIG. 12 c, 8 minutes after the injection only a fewparticles of contrast agent are left in the circulation. This time, theimmobilized particles of contrast agent attached to the thrombus can beseen in the original image too; however, the delineation of the thrombusis not as clear as shown in the processed image.

Finally, at the end of the experiment, ultrasound pulses with highmechanical index (a so-called flash) were used to destroy all theimmobilized particles of contrast agent attached to the thrombus. Theresult so obtained in FIG. 12 d confirms that the enhanced visualizationof the thrombus was totally due to the attached particles of contrastagent.

Moving now to FIG. 13, the main software and hardware components thatcan be used for practicing the solution according to an embodiment ofthe invention are denoted as a whole with the reference 1300. Theinformation (programs and data) is typically stored on the hard disk andloaded (at least partially) into the working memory when the programsare running, together with an operating system and other applicationprograms (not shown in the figure). The programs are initially installedonto the hard disk, for example, from CD-ROM.

Particularly, a driver 1303 controls the imaging probe (not shown in thefigure); for example, the imaging probe driver 1303 includes a transmitbeam former and pulsers for generating the ultrasound pulses to beapplied to the body-part under analysis. The corresponding (analog RF)echo signal that is received from said body-part is supplied to areceive processor 1306. Typically, the receive processor 1306pre-amplifies the analog RF echo signal and applies a preliminarytime-gain compensation (TGC); the analog RF echo signal is thenconverted into digital values by an Analog-to-Digital Converter (ADC),and combined into a focused signal through a receive beam former. Thedigital signal so obtained is preferably processed through furtherdigital algorithms and other linear or non-linear signal conditioners(such as a post-beam-forming TGC). Particularly, the receive processor1306 applies a contrast-specific algorithm to suppress the contributionof the tissues (such as based on the above-mentioned HI, PI, PM or CPStechniques). The digital signal is then demodulated, log-compressed, andscan-converted into a video format. This process results in therecording of a sequence of (video) input images Ii. More specifically,each pixel value of the input images Ii is determined by the intensityof the acoustical response at the location in the body-partcorresponding to said pixel. Optionally, the receive processor 1306 alsoincludes a motion compensation module, carrying out a method forreducing the misalignment of the input images Ii with respect to areference image (for example, due to motion of the patient, to his/herrespiratory cycle or to the involuntary movement of the imaging probe);an example of motion compensation method that is well suited for thispurpose is described in the co-pending International patent applicationPCT/EP2005/053871 filed on 5 Aug. 2005, the entire disclosure of whichis herein incorporated by reference.

A drawing module 1309 is used to predefine a region-of-interest for theanalysis process on the (possibly aligned) input images Ii. Theoperation generates a limitation mask Ml, which consists of a matrix ofbinary values with the same size as the input images Ii (i.e., M×N); allbinary values inside the region of interest are assigned the logic value1, whereas the binary values outside the region of interest are assignedthe logic value 0.

A pre-processor 1312 is optionally used to convert the (video) inputimages into corresponding linearized input images, wherein each pixelvalue is directly proportional to the corresponding local concentrationof the contrast agent; for example, this result can be achieved byapplying an inverse log-compression and then squaring the value soobtained (for example, as described in WO-A-2004/110279, the entiredisclosures of which is herein incorporated by reference). In any case,the pre-processor 1312 outputs a sequence of pre-processed input imagesIp, which consists of the original (video) input images Ii or of thecorresponding linearized input images (according to the user's choice).

A selector 1315 is used to extract and latch one of the pre-processed(video or linearized) input images Ip to be used as a background image(denoted with Ib); for example, the background image Ib is selectedamong the pre-processed input images Ip taken before the contrast agenthas reached the body-part under analysis.

A multiplier operator 1321 receives the background image Ib (from theselector 1315) and the limitation mask Ml (from the drawing module1309). The operator 1321 multiplies the background image Ib by thelimitation mask Ml pixel-by-pixel, so as to generate a correspondinglimited background image LIb (this operation needs to be done only once,but it can be repeated any time during the process). Another multiplieroperator 1324 receives the pre-processed (video or linearized) inputimages Ip (from the pre-processor 1312) and the limitation mask Ml (fromthe drawing module 1309). The operator 1324 multiplies the pre-processedinput images Ip by the limitation mask Ml pixel-by-pixel, so as togenerate corresponding limited input images LIp. As a result, thelimited background image LIb and the limited input images LIp onlyinclude the pixel values of the background image Ib and of thepre-processed input images Ip, respectively, that are inside the regionof interest (defined by the limitation mask Ml), while the other pixelvalues are reset to 0.

A difference operator 1327 receives the limited background image LIb(from the multiplier 1321) and the limited input images LIp (from themultiplier 1324). The operator 1327 subtracts the limited backgroundimage LIb from the limited input images LIp pixel-by-pixel, so as toremove any residual clutter (for example, due the contribution of thetissues that has not been completely removed by the contrast-specificalgorithm applied in the receive processor 1306). The operationgenerates a sequence of corrected images Ic, which are provided to aspatial sub-sampler 1333.

The module 1333 sub-samples the corrected images Ic according to afactor determined by the spatial resolution of the imaging probe alongeach dimension (equivalent to, for example, 2 to 6 pixels). Preferably,the spatial sub-sampling comprises low-pass filtering followed bysub-sampling according to a sub-sampling factor. The cutoff frequency ofthe low-pass filter can be chosen as the highest frequency componentcontaining significant energy in one image selected from the correctedimages Ic, for example, determined by Fourier analysis. The sub-samplingfactor can then be determined, for example, as a value resulting in aspatial sampling frequency equal to twice the cutoff frequency. Eachcorrected image Ic is thus transformed into a (spatially sub-sampled)image Is.

The images Is are stored in succession into a stack 1336, which acts asa buffer memory for further processing according to the above-describedalgorithms. The stack 1336 provides storage for m images Is. The valueof m is determined by the choice of the filtering depth n and a temporalsub-sampling parameter p (ranging from 0 to n−2), according to therelation m=n(p+1). A set of n (temporally sub-sampled) images SIs isthus created and made available for further processing. In mostpractical situations, the parameter p is set to 0 so that m=n. The setof images SIs then consists of all the last n images Is stored in thestack 1336 (so that every image Is is considered). Conversely, when thesub-sampling parameter p is larger than 0, m images Is (m>n) must bestored in the stack 1336, in order to make n images Is available forfurther processing. This temporal sub-sampling may be advantageouslyexploited when the ultrasound scanner works at ultra-high sampling rates(for example, 100-500 images per second), in which case an analysis ofevery available image Is does not provide any useful benefit. The stack1336 also receives an overlapping parameter o (ranging from 0 to m−1).Typically, the overlapping parameter o=m−1, so that the stack 1336creates a new set of images SIs for each image Is. When the overlappingparameter o<m−1, the stack 1336 creates a new set of images SIs afterm−o images Is have entered the stack 1336.

At the same time, the original (video) input images Ii provided by thereceive processor 1306 are latched into another stack 1337, whichconsists of a first-in-first-out (FIFO) shift register, with a sizeequal to m (so as to store m input images Ii).

A filter 1339 receives the set of images SIs from the stack 1336. Thefilter 1339 calculates a filtered image Ifs by applying theabove-described modified Min_IP algorithm on the set of images Sis(comprising n images Is).

The filtered image Ifs so obtained is then passed to a mask generator1342, which also receives a predefined threshold value TH for the cellvalues (for example, ranging from 0 to 5% of their maximum allowablevalue). The mask generator 1342 creates a corresponding overlay mask Mo;the overlay mask Mo is obtained from the filtered image Ifs by assigning(to each cell) the logic value 1 if its value is strictly higher thanthe threshold value TH or the logic value 0 otherwise.

The overlay mask Mo is subsequently provided to a spatial-interpolator1345. The spatial-interpolator 1345 restores the full-size of theoverlay mask Mo corresponding to the size of the input images Ii (i.e.,M×N binary values); for this purpose, the value of each cell in theoverlay mask Mo is replicated for the corresponding group of pixels. Theoperation generates a corresponding interpolated mask RMo.

At the same time, the filtered image Ifs is also provided to apost-processor 1348. The post-processor 1348 optionally converts thecell values of the filtered image Ifs into corresponding discrete values(for example, consisting of 64 or 128 levels that are uniformlydistributed between the lowest value and the highest value of all thecells), by possibly applying a gain factor. Optionally, when the inputimages Ii are linearized by the pre-processor 1312, the post-processor1348 may also apply a non-linear processing (such as a log-compression)so as to produce images with well-balanced contrast. The post-processor1348 also accesses a color look-up table 1351. The color look-up table1351 associates all the possible levels with the representation ofcorresponding colors (that are preferably brighter as the levelsincrease); for example, each color is defined by an index for accessinga location within a palette containing its actual specification. In thisway, each cell in the filtered image Ifs is assigned the correspondingcolor representation.

The filtered image Ifs (either post-processed or as originally built) isprovided to a spatial-interpolator 1354. The spatial-interpolator 1354restores the full-size of the filtered image Ifs corresponding to thesize of the input images Ii (i.e., M×N pixel values) by means ofinterpolation techniques (such as based on the nearest neighbor,bilinear, or bicubic technique). For this purpose, the value of eachcell in the filtered image Ifs is replicated for the corresponding groupof pixels (nearest neighbor interpolation method) and optionallyfiltered spatially (such as using a low-pass 2D or 3D spatial filter).The operation generates a corresponding interpolated image RI.

A multiplier operator 1357 receives the interpolated image RI (from thespatial interpolator 1354) and the interpolated mask RMo (from thespatial interpolator 1345). The operator 1357 multiplies theinterpolated image RI by the interpolated mask RMo pixel-by-pixel, so asto obtain a masked image MI; as a result, the masked image MI onlyincludes the pixel values of the corresponding interpolated image RIthat are higher than the threshold value TH (while the other pixelvalues are reset to 0). The threshold value TH allows tuning the levelof masking of the interpolated image RI; particularly, when thethreshold value TH is set to 0, every pixel of the overlay mask Mo andof the interpolated overlay mask RMo is at the logic value 1, so thatthe masked image MI will be exactly the same as the interpolated imageRI. The masked image MI is then latched into a single-image buffer 1358(replacing its previous content). In this way, the masked image MI inthe buffer 1358 is updated whenever a new filtered image Ifs is outputby the filter 1339, while it remains unchanged otherwise (so as tomaintain the masked image MI that was obtained from the filtered imageIfs last calculated).

The interpolated mask RMo is also supplied from the spatial interpolator1345 to an inverter 1360, which generates a corresponding invertedinterpolated mask RMo (by exchanging the logic values 0 and 1). Theinterpolated mask RMo is likewise latched into another single-imagebuffer 1361 (replacing its previous content), so as to be alwayssynchronized with the masked image MI in the buffer 1358. The invertedinterpolated mask RMo latched in the buffer 1361 is then passed to amultiplier operator 1363. The multiplier operator 1363 also receives adelayed image Id from the stack 1337. Every time the invertedinterpolated mask RMo is latched into the buffer 1361, the correspondingdelayed image Id exits from the stack 1337, thus allowing the operator1363 to multiply its inputs, pixel-by-pixel, so as to obtain a maskeddelayed image MId; as a result, the masked delayed image MId onlyincludes the pixel values of the delayed image Id that are not includedin the corresponding masked image MI (while the other pixel values arereset to 0).

An adder operator 1369 receives the masked delayed image MId (from themultiplier 1363) and the masked image MI (latched in the buffer 1358).The operator 1369 adds the masked image MI and the masked delayed imageMId pixel-by-pixel (correctly synchronized) so as to obtain an overlaidimage Io. In this way, each pixel value of the delayed image Id isoverridden by the corresponding pixel value of the masked image MI ifand only if the latter has a significant value (i.e., higher than thethreshold value TH).

The overlaid image Io is passed to a monitor driver 1372, which controlsits visualization. At the same time, the overlaid image Io may also beadded to a repository 1375. The same operations described above arereiterated for each new input image Ii that is recorded. Particularly,the new input image Ii is pushed into the stack 1337; this causes theshifting of the preceding input images Ii in the stack 1337, and theoutput of the oldest one. At the same time (after the possiblelinearization, the limitation to the desired region of interest, thesubtraction of the background image Ib, and the spatial sub-sampling)the corresponding new corrected image Ic is added to the stack 1336. Asa result, the overlaid images Io are displayed in succession on themonitor of the ultrasound scanner; it should be noted that each overlaidimage Io is available with a delay (with respect to its acquisitiontime), which is defined by the time required to cross the whole stack1337 (corresponding to the selected filtering window). Moreover, thesequence of overlaid images Io so obtained is also available in therepository 1375 for further analysis.

The solution described above facilitates the detection of theimmobilized contrast agent; particularly, this allows discriminating theimmobilized contrast agent from the circulating contrast agent (and frommoving tissues as well). Therefore, the accuracy of any analysis of theobtained results is strongly increased.

More specifically, this technique makes it possible to spatiallydelineate the immobilized contrast agent in the region of interest ofthe body-part; at the same time, it allows quantifying the concentrationof the immobilized contrast agent at each location with a relativelyhigh degree of accuracy. For example, this facilitates the correctdiagnosis of several pathologies that would otherwise be difficult todetect.

It should be noted that the detection of the immobilized contrast agentis now possible in real-time (while the images are displayed).Particularly, the immobilized particles of contrast agent are revealedas soon as they remain attached to the target; therefore, the results ofthe analysis may be available at an early time point after theadministration of the contrast agent (without the need of waiting forits complete wash-out).

All of the above contributes to the successful development of newimaging techniques based on the use of targeted contrast agents.

The reading of the sequence of overlaid images is further facilitatedwhen they are displayed in color, as defined by the colorrepresentations assigned to the cells during the process of building the(interpolated) filtered images. In this case, each different color bearsa quantitative meaning of its own; for example, this value can be readout from a color bar, which is displayed on the monitor close to thesequence of overlaid images.

Moreover, the overlay of the filtered images on the input imagesprovides an enhanced visual perception of the immobilized contrastagent, which is now contextualized on the actual representation of thebody-part under analysis. It should also be noted that the thresholdvalue TH allows tuning the degree of the overlay according to contingentrequirements. For example, an increase of the threshold value TH reducesthe overlay, so as to limit the impact of the process on the (original)input images. Conversely, by setting the threshold value TH to 0 it ispossible to superimpose the filtered images completely onto the inputimages in the region of interest.

MODIFICATIONS

Naturally, in order to satisfy local and specific requirements, a personskilled in the art may apply to the solution described above manymodifications and alterations. Particularly, although the presentinvention has been described with a certain degree of particularity withreference to preferred embodiment(s) thereof, it should be understoodthat various omissions, substitutions and changes in the form anddetails as well as other embodiments are possible; moreover, it isexpressly intended that specific elements and/or method steps describedin connection with any disclosed embodiment of the invention may beincorporated in any other embodiment as a general matter of designchoice.

For example, similar considerations apply if the ultrasound scanner hasa different structure or includes other units (such as with an imagingprobe of the linear-, convex-, phased-, or matrix-array type).Alternatively, the proposed solution is applied in a medical imagingsystem that consists of an ultrasound scanner and a distinct computer(or any equivalent data processing system); in this case, the measureddata is transferred from the ultrasound scanner to the computer for itsprocessing (for example, through a removable disk, a memory key, or anetwork connection). In any case, the application to any other medicalimaging application, such as based on Magnetic Resonance Imaging (MRI)or X-ray Computed Tomography (CT), is within the scope of the invention.

Likewise, the solution of the invention lends itself to be put intopractice with equivalent contrast agents for whatever (biological)target; for example, in the above-mentioned alternative applications,the contrast agent can be specific for enhancing Magnetic Resonanceimaging or X-ray Computed Tomography imaging.

In any case, nothing prevents the application of the proposed processingto the whole images (without selecting any region of interest).

As described above, the solution of the invention is preferablyimplemented by calculating the minimum among the relevant pixel values(so as to facilitate removing the effects of overlapping speckle grainsgenerated by the circulating particles of contrast agent). However, thisis not to be intended as a limitation; indeed, alternative algorithmsmay be used to calculate values generically indicative of the lowestvalues in the relevant set (for example, based on weighted averages ofthe pixel values). More generally, the proposed solution replaces thepixel values in the input images with filtered values that arerepresentative of the lowest response (to the ultrasound pulses) of thecorresponding pixel locations; therefore, in a system based on negativeimages (wherein the pixel values decrease with the intensity of the echosignal) this would mean calculating the maximum in the set.

Without departing from the principles of the invention, the filteringwindow (and then the filtering length) may also be defined in adifferent way. In any case, nothing prevents the use of a predefinedfiltering window (for example, set to a value providing acceptableperformance in most standard situations).

Alternatively, the temporal sub-sampling of the input images may beperformed according to any other criteria (or it may be omittedaltogether).

Moreover, any other technique for acquiring the input images is withinthe scope of the present invention (for example, using Doppler-basedalgorithms).

It should be appreciated that the feature relating to the subtraction ofthe background image is not strictly necessary (and it may be omitted insome implementations of the invention).

Similar considerations apply if the input images are sub-sampled with adifferent technique (for example, according to a predefined sub-samplingfactor); in any case, the application of the proposed solution at thepixel level (instead of at the level of groups of pixels defined by theabove-mentioned spatial sub-sampling) is not excluded.

Likewise, it is also possible to omit compensating the motion of theinput image (for example, when this motion is far slower than the flowof the contrast agent).

Alternatively, the spatial sub-sampling may be applied after filteringthe images, instead of being applied before their storing into thecorresponding stack.

It is also possible to leave the choice of overlaying the filteredimages on the input images to the preference of a user. For example, thefiltered images may be displayed alone without being overlaid on theinput images or the pixel values of the input images within the regionof interest may be set to zero in order to display the filtered imagesagainst a black background that may improve contrast. According to analternative embodiment, the filtered images are overlaid on noncontrast-specific images (such as fundamental B-mode images extractedfrom the contrast-specific images provided by the receive processor),the filtered images being nevertheless preferably generated from imagesprovided by a contrast-specific imaging mode. Moreover, although thepresent invention has been specifically designed for use in real-time,the application of the devised solution for analyzing the obtainedresults off-line is contemplated.

The method can be extended by the implementation of two or more filtersin parallel that apply the modified Min_IP algorithm with differentfiltering lengths. This would enable identifying particles of contrastagent with different flow rates at the same time (such as fast moving,slow moving and immobilized particles of contrast agent).

Similar considerations apply if the program (which may be used toimplement the invention) is structured in a different way, or ifadditional modules or functions are provided; likewise, the memorystructures may be of other types, or may be replaced with equivalententities (not necessarily consisting of physical storage media).Moreover, the proposed solution lends itself to be implemented with anequivalent method (having similar or additional steps, even in adifferent order). In any case, the program may take any form suitable tobe used by or in connection with any data processing system, such asexternal or resident software, firmware, or microcode (either in objectcode or in source code). Moreover, the program may be provided on anycomputer-usable medium; the medium can be any element suitable tocontain, store, communicate, propagate, or transfer the program.Examples of such medium are fixed disks (where the program can bepre-loaded), removable disks, tapes, cards, wires, fibers, wirelessconnections, networks, broadcast waves, and the like; for example, themedium may be of the electronic, magnetic, optical, electromagnetic,infrared, or semiconductor type.

In any case, the solution according to the present invention lendsitself to be carried out with a hardware structure (for example,integrated in a chip of semiconductor material), or with a combinationof software and hardware.

1. A system for facilitating the detection of an immobilized contrastagent in medical imaging applications, the system including: means forproviding a sequence of a total number of input images obtained atcorresponding acquisition instants by imaging a body-part of a patientsubjected to an administration of a contrast agent capable ofcirculating within the patient and of being substantially immobilized ona biological target, each input image including a plurality of inputvalues each one indicative of a response to an interrogation signal of acorresponding portion of the body-part possibly including said contrastagent, and means for reducing a contribution of the circulating contrastagent within the body-part in at least one selected input image, whereinthe means for reducing includes means for creating a filtered imagecorresponding to each selected input image by replacing a set of inputvalues of the selected input image with a set of corresponding filteredvalues, each filtered value being representative of the lowest responseof the corresponding portion of the body-part in a set of multiple inputimages including the selected input image, the set of multiple inputimages consisting of a number of input images lower than the totalnumber.
 2. The system according to claim 1, wherein each input valueincreases with the response to the interrogation signal of thecorresponding portion of the body-part, each filtered value consistingof the minimum among the corresponding input values in the set ofmultiple input images.
 3. The system according to claim 1, wherein theset of multiple input images consists of the selected input image and atleast one preceding input image in the sequence.
 4. The system accordingto claim 3, wherein the at least one preceding input image consists of aplurality of preceding input images.
 5. The system according to claim 3,further including: means for selecting the number of preceding inputimages as a function of an estimated flow rate of the contrast agent inthe body-part.
 6. The system according to claim 1, further includingmeans for temporally sub-sampling the preceding input images.
 7. Thesystem according to claim 1, wherein the body-part includes a tissue,the means for providing the sequence of input images including: meansfor reducing a contribution of the tissue in the input images.
 8. Thesystem according to claim 1, wherein the means for creating the filteredimage includes: means for selecting a background image in the sequenceof input images, and means for subtracting the background image from theset of multiple input images.
 9. The system according to claim 1,wherein the means for creating the filtered image includes: means forspatially sub-sampling the set of multiple input images according to anestimated resolution thereof.
 10. The system according to claim 1,further including means for selecting a reference image in the sequenceof input images and for compensating a motion of each input image withrespect to the reference image.
 11. The system according to claim 1,further including means for linearizing the set of multiple input imagesto make each input value thereof substantially proportional to aconcentration of the contrast agent in the corresponding portion of thebody-part.
 12. The system according to claim 1, further including: meansfor displaying each filtered image substantially in real-time with theacquisition instant of the corresponding selected input image.
 13. Thesystem according to claim 1, wherein the means for creating the filteredimage includes: means for resetting each filtered value in the filteredimage not reaching a threshold value, and means for overlaying thefiltered image on the corresponding selected input image.
 14. A methodfor facilitating the detection of an immobilized contrast agent inmedical imaging applications, the method including the steps of:providing a sequence of a total number of input images obtained atcorresponding acquisition instants by imaging a body-part of a patientsubjected to an administration of a contrast agent capable ofcirculating within the patient and of being substantially immobilized ona biological target, each input image including a plurality of inputvalues each one indicative of a response to an interrogation signal of acorresponding portion of the body-part possibly including said contrastagent, and reducing a contribution of the circulating contrast agentwithin the body-part in at least one selected input image, wherein foreach selected input image the step of reducing includes creating acorresponding filtered image by replacing a set of input values of theselected input image with a set of corresponding filtered values, eachfiltered value being representative of the lowest response of thecorresponding portion of the body-part in a set of multiple input imagesincluding the selected input image, the set of multiple input imagesconsisting of a number of input images lower than the total number. 15.A computer program for performing the method of claim 14 when thecomputer program is executed on a data processing system.
 16. A computerprogram product including a computer-usable medium embodying a computerprogram, the computer program when executed on a data processing systemcausing the system to perform a method for facilitating the detection ofan immobilized contrast agent in medical imaging applications, whereinthe method includes the steps of: providing a sequence of a total numberof input images obtained at corresponding acquisition instants byimaging a body-part of a patient subjected to an administration of acontrast agent capable of circulating within the patient and of beingsubstantially immobilized on a biological target, each input imageincluding a plurality of input values each one indicative of a responseto an interrogation signal of a corresponding portion of the body-partpossibly including said contrast agent, and reducing a contribution ofthe circulating contrast agent within the body-part in at least oneselected input image, wherein for each selected input image the step ofreducing includes: creating a corresponding filtered image by replacinga set of input values of the selected input image with a set ofcorresponding filtered values, each filtered value being representativeof the lowest response of the corresponding portion of the body-part ina set of multiple input images including the selected input image, theset of multiple input images consisting of a number of input imageslower than the total number.